Table REF  shows the architectures of visual and audio CNNs we use for our model.
For the visual CNN, we use the SlowFast network [1]} with a ResNet-50 backbone [2]}.
We use the speed ratio \(\alpha =8\)  and the channel ratio \(\beta =1/8\)  for the SlowFast architecture, so \(T_{f} = 8 \times T_{s}\) .
We use different values of \(T_{s}\)  and \(T_{f}\)  for different tasks.
During pretraining, we set \(T_{s}=4\)  and \(T_{f}=32\) .
During finetuning, we use \(T_{s}=8\)  and \(T_{f}=64\)  for short-video action classification on UCF101 [3]} while we use \(T_{s}=4\)  and \(T_{f}=32\)  for long-video action classification on Charades [4]} and Kinetics-Sounds [5]}.
For the audio CNN, we use a ResNet-50 without the downsampling layer pool\(_{1}\)  to preserve information along both frequency and time axis in early stages.
We use different values of \(T_{a}\)  for different training phases.
We set \(T_{a}=220\)  for one-second clip during pretraining while we use \(T_{a} = 440\)  for two-second clip during finetuning.
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